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IMP Reference Guide  2.6.0
The Integrative Modeling Platform
cluster.py
1 #!/usr/bin/env python
2 
3 from __future__ import print_function
4 from IMP import OptionParser
5 import itertools
6 import math
7 import IMP.multifit
8 import IMP.container
9 
10 __doc__ = "Cluster assembly solutions."
11 
12 def get_uniques(seq):
13  # Not order preserving
14  keys = {}
15  counter = []
16  for e in seq:
17  keys[e] = 1
18  num_keys = len(list(keys.keys()))
19  for i in range(num_keys + 1):
20  counter.append([0, i])
21  for e in seq:
22  counter[e][0] = counter[e][0] + 1
23  counter = sorted(counter, reverse=True)
24  indexes = []
25  for c, i in counter[:-1]:
26  indexes.append(i)
27  return indexes
28 
29 
30 class ClusterData:
31 
32  def __init__(self, cluster_ind, cluster_size, rmsd_calculated):
33  self.cluster_ind = cluster_ind
34  self.cluster_size = cluster_size
35  self.rmsd_calculated = rmsd_calculated
36 
37  def set_distance_stats(self, avg, std):
38  self.distance_avg = avg
39  self.distance_std = std
40 
41  def set_angle_stats(self, avg, std):
42  self.angle_avg = avg
43  self.angle_std = std
44 
45  def set_rmsd_stats(self, avg, std):
46  self.rmsd_avg = avg
47  self.rmsd_std = std
48 
49  def set_best_sampled_data(self, ind, rmsd, cc, distance, angle):
50  self.best_sampled_ind = ind
51  self.best_sampled_rmsd = rmsd
52  self.best_sampled_cc = cc
53  self.best_sampled_distance = distance
54  self.best_sampled_angle = angle
55 
56  def set_best_scored_data(self, ind, rmsd, cc, distance, angle):
57  self.best_scored_ind = ind
58  self.best_scored_rmsd = rmsd
59  self.best_scored_cc = cc
60  self.best_scored_distance = distance
61  self.best_scored_angle = angle
62 
63 
65 
66  """
67  Clusters assembly models
68  - The solutions are chosen by sorting the database according to the
69  parameter orderby
70  - The models are aligned and clustered by RMSD
71  """
72 
73  def __init__(self, asmb_fn, prot_fn, map_fn, align_fn, combs_fn):
74  self.asmb_fn = asmb_fn
75  self.prot_fn = prot_fn
76  self.map_fn = map_fn
77  self.align_fn = align_fn
78  self.combs_fn = combs_fn
79 
80  self.asmb = IMP.multifit.read_settings(self.asmb_fn)
81  self.asmb.set_was_used(True)
82  self.prot_data = IMP.multifit.read_proteomics_data(self.prot_fn)
83  self.alignment_params = IMP.multifit.AlignmentParams(self.align_fn)
85  self.prot_data, self.map_fn)
86 
88  self.mapping_data, self.asmb,
89  self.alignment_params)
90  self.align.set_was_used(True)
91 
92  self.combs = IMP.multifit.read_paths(self.combs_fn)
93  self.ensmb = IMP.multifit.Ensemble(self.asmb, self.mapping_data)
94  self.ensmb.set_was_used(True)
95  self.mhs = self.align.get_molecules()
96  for i, mh in enumerate(self.mhs):
97  self.ensmb.add_component_and_fits(mh,
98  IMP.multifit.read_fitting_solutions(self.asmb.get_component_header(i).get_transformations_fn()))
99  # load the density map
100  self.dmap = IMP.em.read_map(
101  self.asmb.get_assembly_header().get_dens_fn())
102  self.dmap.set_was_used(True)
103  self.dmap.get_header().set_resolution(
104  self.asmb.get_assembly_header().get_resolution())
105  threshold = self.asmb.get_assembly_header().get_threshold()
106  self.dmap.update_voxel_size(
107  self.asmb.get_assembly_header().get_spacing())
108  self.dmap.set_origin(self.asmb.get_assembly_header().get_origin())
109  self.dmap.calcRMS()
110 
111  def do_clustering(self, max_comb_ind, max_rmsd):
112  """
113  Cluster configurations for a model based on RMSD.
114  An IMP.ConfigurationSet is built using the reference frames for
115  all of the components of the assembly for each solution
116  @param max_comb_ind Maximum number of components to consider
117  @param max_rmsd Maximum RMSD tolerated when clustering
118  """
119  import fastcluster
120  import scipy.cluster.hierarchy
121 
122  self.mdl = self.align.get_model()
123  self.all_ca = []
124  for mh in self.mhs:
125  mh_res = IMP.atom.get_by_type(mh, IMP.atom.RESIDUE_TYPE)
126  s1 = IMP.atom.Selection(mh_res)
127  s1.set_atom_types([IMP.atom.AtomType("CA")])
128  self.all_ca.append(s1.get_selected_particles())
129  # load configurations
130  self.coords = []
131  print("load configurations")
132  for combi, comb in enumerate(self.combs[:max_comb_ind]):
133  self.ensmb.load_combination(comb)
134  c1 = []
135  for mol_ca in self.all_ca:
136  mol_xyz = []
137  for ca in mol_ca:
138  mol_xyz.append(IMP.core.XYZ(ca).get_coordinates())
139  c1.append(mol_xyz)
140  self.coords.append(c1)
141  self.ensmb.unload_combination(comb)
142  self.distances = []
143  print("calculate distances")
144  for i in range(len(self.coords)):
145  for j in range(i + 1, len(self.coords)):
146  self.distances.append(
148  list(itertools.chain.from_iterable(self.coords[i])),
149  list(itertools.chain.from_iterable(self.coords[j]))))
150  print("cluster")
151  Z = fastcluster.linkage(self.distances)
152  self.cluster_inds = scipy.cluster.hierarchy.fcluster(
153  Z, max_rmsd, criterion='distance')
154  self.uniques = get_uniques(self.cluster_inds)
155  print("number of clusters", len(self.uniques))
156 
157  # return clusters by their size
158  return self.uniques
159 
161  self, model_coords, native_coords):
162  """
163  Computes the position error (placement distance) and the orientation error (placement angle) of the coordinates in model_coords respect to the coordinates in native_coords.
164  placement distance - translation between the centroids of the
165  coordinates placement angle - Angle in the axis-angle formulation of the rotation
166  aligning the two rigid bodies.
167  """
168  native_centroid = IMP.algebra.get_centroid(native_coords)
169  model_centroid = IMP.algebra.get_centroid(model_coords)
170  translation_vector = native_centroid - model_centroid
171  distance = translation_vector.get_magnitude()
172  if(len(model_coords) != len(native_coords)):
173  raise ValueError(
174  "Mismatch in the number of members %d %d " % (
175  len(model_coords),
176  len(native_coords)))
178  model_coords,
179  native_coords)
180  P = IMP.algebra.get_axis_and_angle(TT.get_rotation())
181  angle = P.second * 180. / math.pi
182  return distance, angle
183 
184  def get_cc(self, ps):
185  '''
186  bb_native = self.dmap.get_bounding_box()
187  bb_solution = IMP.core.get_bounding_box(IMP.core.XYZs(ps))
188  # bounding box enclosing both the particles of the native assembly
189  # and the particles of the model
190  bb_union = IMP.algebra.get_union(bb_native, bb_solution)
191  # add border of 4 voxels
192  border = 4*voxel_size
193  bottom = bb_union.get_corner(0)
194  bottom += IMP.algebra.Vector3D(-border, -border, -border)
195  top = bb_union.get_corner(1)
196  top += IMP.algebra.Vector3D(border, border, border)
197  bb_union = IMP.algebra.BoundingBox3D(bottom, top)
198  '''
199 
200  resolution = self.dmap.get_header().get_resolution()
201  voxel_size = self.dmap.get_spacing()
202 
203  map_solution = IMP.em.SampledDensityMap(self.dmap.get_header())
204  map_solution.set_particles(ps)
205  map_solution.resample()
206  map_solution.set_was_used(True)
207 
208  map_solution.calcRMS()
209  coarse_cc = IMP.em.CoarseCC()
210  coarse_cc.set_was_used(True)
211  # base the calculation of the cross_correlation coefficient on the threshold
212  # for the native map, because the threshold for the map of the model changes
213  # with each model
214  # map_solution.get_header().show()
215  threshold = 0.01 # threshold AFTER normalization using calcRMS()
216  ccc = coarse_cc.cross_correlation_coefficient(map_solution,
217  self.dmap, threshold)
218  return ccc
219 
220  def get_cluster_representative_combination(self, query_cluster_ind):
221  return self.combs[self.clusters_data[query_cluster_ind].cluster_ind]
222 
223  def get_cluster_stats(self, query_cluster_ind):
224  return self.clusters_data[query_cluster_ind]
225 
226  def do_analysis(self, max_comb_ind):
227  self.clusters_data = {}
228  for cluster_ind in self.uniques:
229  self.clusters_data[cluster_ind] = self.analyze_cluster(
230  cluster_ind, max_comb_ind)
231 
232  def analyze_cluster(self, query_cluster_ind, max_comb_ind):
233  # load reference
234  mhs_native = []
235  mhs_native_ca = []
236  mhs_native_ca_ps = []
237  calc_rmsd = True
238  for i in range(len(self.mhs)):
239  if self.asmb.get_component_header(i).get_reference_fn() == "":
240  calc_rmsd = False
241  continue
242  mhs_native.append(
244  self.asmb.get_component_header(
245  i).get_reference_fn(
246  ),
247  self.mdl))
248  s1 = IMP.atom.Selection(mhs_native[-1])
249  s1.set_atom_types([IMP.atom.AtomType("CA")])
250  mhs_native_ca.append([])
251  mhs_native_ca_ps.append([])
252  for p in s1.get_selected_particles():
253  mhs_native_ca[-1].append(IMP.core.XYZ(p).get_coordinates())
254  mhs_native_ca_ps[-1].append(IMP.core.XYZ(p))
255 
256  rmsds = []
257  distances = []
258  angles = []
259  for i in range(len(self.mhs)):
260  distances.append([])
261  angles.append([])
262  best_sampled_ind = -1
263  best_scored_ind = -1
264  voxel_size = 3 # check with javi
265  resolution = 20 # check with javi
266  counter = -1
267  for elem_ind1, cluster_ind1 in enumerate(self.cluster_inds):
268  if cluster_ind1 != query_cluster_ind:
269  continue
270  counter = counter + 1
271  if calc_rmsd:
272  rmsds.append(
274  list(itertools.chain.from_iterable(mhs_native_ca)),
275  list(itertools.chain.from_iterable(self.coords[elem_ind1]))))
276  if best_scored_ind == -1:
277  self.ensmb.load_combination(self.combs[elem_ind1])
278  best_scored_ind = counter
279  best_scored_cc = self.get_cc(
280  list(itertools.chain.from_iterable(self.all_ca)))
281  if calc_rmsd:
282  best_scored_rmsd = rmsds[-1]
283  best_sampled_ind = counter
284  best_sampled_cc = best_scored_cc
285  best_sampled_rmsd = rmsds[-1]
286  self.ensmb.unload_combination(self.combs[elem_ind1])
287  # print rmsds[-1],best_scored_rmsd
288  if calc_rmsd:
289  if rmsds[-1] < best_scored_rmsd:
290  self.ensmb.load_combination(self.combs[elem_ind1])
291  best_sampled_ind = counter
292  best_sampled_cc = self.get_cc(
293  list(itertools.chain.from_iterable(self.all_ca)))
294  best_sampled_rmsd = rmsds[-1]
295  self.ensmb.unload_combination(self.combs[elem_ind1])
296  sum_d = 0
297  sum_a = 0
298  for i in range(len(self.mhs)):
300  self.coords[elem_ind1][i],
301  mhs_native_ca[i])
302  sum_d = sum_d + d
303  sum_a = sum_a + a
304  distances[i].append(sum_d / len(self.mhs))
305  angles[i].append(sum_a / len(self.mhs))
306  cd = ClusterData(query_cluster_ind, counter + 1, calc_rmsd)
307  if calc_rmsd:
308  import numpy
309  d = numpy.array(list(itertools.chain.from_iterable(distances)))
310  a = numpy.array(list(itertools.chain.from_iterable(angles)))
311  r = numpy.array(rmsds)
312  cd.set_distance_stats(d.mean(), d.std())
313  cd.set_angle_stats(a.mean(), a.std())
314  cd.set_best_scored_data(
315  best_scored_ind,
316  best_scored_rmsd,
317  best_scored_cc,
318  d[0],
319  a[0])
320  cd.set_rmsd_stats(r.mean(), r.std())
321  cd.set_best_sampled_data(
322  best_sampled_ind,
323  best_sampled_rmsd,
324  best_sampled_cc,
325  d[best_sampled_ind],
326  a[best_sampled_ind])
327  else:
328  cd.set_best_scored_data(
329  best_scored_ind,
330  -1,
331  best_scored_cc,
332  -1,
333  -1)
334  return cd
335 
336 
337 def usage():
338  usage = """%prog [options] <asmb> <asmb.proteomics> <asmb.mapping>
339  <alignment.params> <combinations> <output: clustered combinations>
340 
341 Clustering of assembly solutions.
342 
343 This program uses the Python 'fastcluster' module, which can be obtained from
344 http://math.stanford.edu/~muellner/fastcluster.html
345 """
346  parser = OptionParser(usage)
347  parser.add_option("-m", "--max", type="int", dest="max", default=999999999,
348  help="maximum solutions to consider")
349  parser.add_option("-r", "--rmsd", type="float", dest="rmsd", default=5,
350  help="maximum rmsd within a cluster")
351  options, args = parser.parse_args()
352  if len(args) != 6:
353  parser.error("incorrect number of arguments")
354  return options, args
355 
356 
357 def main():
358  IMP.set_log_level(IMP.WARNING)
359  options, args = usage()
360  asmb_fn = args[0]
361  prot_fn = args[1]
362  map_fn = args[2]
363  align_fn = args[3]
364  combs_fn = args[4]
365  output_fn = args[5]
366 
367  clust_engine = AlignmentClustering(
368  asmb_fn,
369  prot_fn,
370  map_fn,
371  align_fn,
372  combs_fn)
373  clusters = clust_engine.do_clustering(options.max, options.rmsd)
374  cluster_representatives = []
375  print("clustering completed")
376  print("start analysis")
377  clust_engine.do_analysis(options.max)
378  repr_combs = []
379  for cluster_ind in clust_engine.uniques:
380  repr_combs.append(
381  clust_engine.get_cluster_representative_combination(cluster_ind))
382  IMP.multifit.write_paths(repr_combs, output_fn)
383  # print the clusters data
384  for cluster_ind in clust_engine.uniques:
385  info = clust_engine.get_cluster_stats(cluster_ind)
386  repr_combs.append(
387  clust_engine.get_cluster_representative_combination(cluster_ind))
388  print("==========Cluster index:", info.cluster_ind, "size:", info.cluster_size)
389  if info.rmsd_calculated:
390  print("best sampled in cluster (index,cc,distance,angle,rmsd):", info.best_sampled_ind, info.best_sampled_cc, info.best_sampled_distance, info.best_sampled_angle, info.best_sampled_rmsd)
391  if info.rmsd_calculated:
392  print("cluster representative (index,cc,distance,angle,rmsd):", info.best_scored_ind, info.best_scored_cc, info.best_scored_distance, info.best_scored_angle, info.best_scored_rmsd)
393  else:
394  print("cluster representative (index,cc):", info.best_scored_ind, info.best_scored_cc)
395 
396 
397 if __name__ == "__main__":
398  main()
An ensemble of fitting solutions.
Various classes to hold sets of particles.
Clusters assembly models.
Definition: cluster.py:64
void write_paths(const IntsList &paths, const std::string &txt_filename)
The type of an atom.
SettingsData * read_settings(const char *filename)
void read_pdb(TextInput input, int model, Hierarchy h)
ProteinsAnchorsSamplingSpace read_protein_anchors_mapping(multifit::ProteomicsData *prots, const std::string &anchors_prot_map_fn, int max_paths=INT_MAX)
Responsible for performing coarse fitting between two density objects.
Definition: CoarseCC.h:28
IMP-specific subclass of optparse.OptionParser.
Definition: __init__.py:9307
Align proteomics graph to EM density map.
Class for sampling a density map from particles.
double get_rmsd(const Selection &s0, const Selection &s1)
Fitting atomic structures into a cryo-electron microscopy density map.
std::pair< Vector3D, double > get_axis_and_angle(const Rotation3D &rot)
Decompose a Rotation3D object into a rotation around an axis.
A decorator for a particle with x,y,z coordinates.
Definition: XYZ.h:30
ProteomicsData * read_proteomics_data(const char *proteomics_fn)
Proteomics reader.
def get_placement_score_from_coordinates
Computes the position error (placement distance) and the orientation error (placement angle) of the c...
Definition: cluster.py:160
def do_clustering
Cluster configurations for a model based on RMSD.
Definition: cluster.py:111
Vector3D get_centroid(const Vector3Ds &ps)
Returns the centroid of a set of vectors.
Definition: Vector3D.h:56
void set_log_level(LogLevel l)
Set the current global log level.
IntsList read_paths(const char *txt_filename, int max_paths=INT_MAX)
Read paths.
FittingSolutionRecords read_fitting_solutions(const char *fitting_fn)
Fitting solutions reader.
def get_cc
bb_native = self.dmap.get_bounding_box() bb_solution = IMP.core.get_bounding_box(IMP.core.XYZs(ps)) bounding box enclosing both the particles of the native assemblyand the particles of the modelbb_union = IMP.algebra.get_union(bb_native, bb_solution) add border of 4 voxelsborder = 4*voxel_size bottom = bb_union.get_corner(0) bottom += IMP.algebra.Vector3D(-border, -border, -border) top = bb_union.get_corner(1) top += IMP.algebra.Vector3D(border, border, border) bb_union = IMP.algebra.BoundingBox3D(bottom, top)
Definition: cluster.py:184
double get_resolution(Model *m, ParticleIndex pi)
Estimate the resolution of the hierarchy as used by Representation.
Transformation3D get_transformation_aligning_first_to_second(Vector3Ds a, Vector3Ds b)
Select hierarchy particles identified by the biological name.
Definition: Selection.h:66